Phase 7: Speech Synthesis & Applications · ~75 minutes · Python

Voice Anti-Spoofing & Audio Watermarking — ASVspoof 5, AudioSeal, WaveVerify

Voice cloning shipped faster than defenses. 2026 production voice systems need two things: a detector (AASIST, RawNet2) that classifies real vs fake speech, and a watermark (AudioSeal) that survives compression and editing. Ship both or do not ship voice cloning.

What you will learn

Implement voice anti-spoofing & audio watermarking — asvspoof 5, audioseal, waveverify from scratch
Understand the math and intuition behind the algorithm
Use production libraries for the same task
Ship a reusable artifact

Introduction

Type: Build
Languages: Python
Prerequisites: Speaker Recognition & Verification, Voice Cloning & Voice Conversion
Time: ~75 minutes

The Problem

Three related defenses:

Anti-spoofing / deepfake detection. Given an audio clip, is it synthetic or real? ASVspoof benchmarks (ASVspoof 2019 → 2021 → 5) are the gold standard.
Audio watermarking. Embed an imperceptible signal in generated audio that a detector can extract later. AudioSeal (Meta) and WavMark are the open options.
Authenticated provenance. Cryptographic signing of audio files + metadata. C2PA / Content Authenticity Initiative.

Detection handles adversaries who don't cooperate. Watermarking handles compliance — AI-generated audio should be identifiable as such. Both are required in 2026.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Check Yourself, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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